An enhanced least significant bit steganographic method for information hiding

Journal of Information Engineering and Applications www.iiste.org
ISSN 2224-5782 (print) ISSN 2225-0506 (online)
Vol 2, No.9, 2012

An enhanced Least Significant Bit Steganographic Method for
Information Hiding
Gabriel Macharia Kamau1* Stephen Kimani2 Waweru Mwangi2
1. School of Computer Science and Information Technology, Kimathi University College of Technology, PO
box 657-10100, Nyeri, Kenya
2. Institute of Computer Science and Information Technology, Jomo Kenyatta University of Agriculture and
Technology, PO box 62000-00200, Nairobi, Kenya
* E-mail of the corresponding author: ngorosh2003@yahoo.com
Abstract
The least significant bit (LSB) insertion method is a simple steganographic algorithm that takes the least significant
bit in some bytes of the cover medium and swaps them with a sequence of bytes containing the secret data in order to
conceal the information in the cover medium. However its imperceptibility and hiding capacity are relatively low.
This is as revealed by the statistical characteristics of its resultant stego images compared to the original cover
images. To increase the level of imperceptibility and the hiding capacity in the LSB insertion method, this research
proposes an enhanced LSB method that employs a selective and randomized approach in picking specific number of
target image bits to swap with the secret data bits during the embedding process. To facilitate the selective picking of
the target image bits, the standard minimal linear congruential number generator (LCG) is used. The message digest
(digital signature) of a user supplied password is used to seed the LCG and to extract the message from the cover
medium. In measuring the effectiveness of the proposed method, the study adopted an experimental research design
where the statistical characteristics of the proposed method stego images were compared with those of the traditional
LSB method in a comparative experiment designed to establish the levels of image distortion (noise) introduced in
the original cover image when either of the methods is used under the same payload and image. The experiment
results indicated improved levels of imperceptibility and hiding capacity in the proposed method.
Key Words: Steganography, Steganalysis, Stego image, payload, imperceptibility

1. Introduction
According to Mohammad and Abdallah (2008), Steganography “is the art and science of writing hidden messages
inside innocent looking containers such as digital files, in such a way that no one apart from the sender and intended
recipient realizes the existence of the hidden message”. The secret message is normally embedded in a cover medium
known as a stego file in a way that totally conceals the existence of any form of communication going on. Digital
images are the most widely used cover files in the world of digital steganography. The reason for this is because the
human visual system can hardly pick the difference between an original image and a stego image when embedding of
secret information is properly done.

One of the most popular and commonly used steganographic algorithms for information hiding in digital images is
the LSB insertion method. It is a simple algorithm that swaps the least significant bit in some bytes of the cover
medium with a sequence of bytes containing the secret data to be hid. However, though the LSB algorithm hides data
in the cover medium (image) in a way that is imperceptible to the human visual system (HVS), its imperceptibility to
statistical steganalysis is relatively low. This is mainly because the significant bits of the secret message are hidden in
the cover medium in a linear and deterministic pattern. Retrieval of secret data using steganalysis software tools
therefore becomes relatively easy once the algorithm used is known. The following table shows the values of the
statistical (perceptibility metrics values) of stego images produced through the use of the LSB insertion method.
Same payload (a 12 kilo byte document) is hidden in each image.

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Table 1. Perceptibility metrics data for the Traditional LSB Method.

IMAGE A AD MSE SNR PSNR RS SPA

(db) (db) (db) (db) (db) (db)

Banana.jpg 0.1341 0.268 613629 2177125 19.47 19.54

Dancers.jpg 0.1514 0.304 538341 1903976 16.22 17.29

Graduation.jpg 0.6695 1.34 135300 436554 49.74 51.38

Office.jpg 0.151 0.302 696202 1931692 17.46 18.97

zhbackground.bmp 0.5011 1.003 75296 581631 39.33 39.51

An enhanced LSB method that employs a selective and randomized approach in picking the target image bits to swap
with the secret data bits during the embedding process using the standard minimal linear congluential pseudo random
number generator (LCG) is proposed. A hushed stego key (k) value is used as a seed to determine the set of selected
numbers used for targeting specific image bits for data hiding.

2. Related Work
2.1 The Optimal LSB Insertion Method
This insertion method improves the stego-image quality by finding an optimal pixel after performing an adjustment
process. Three candidates are picked out for the pixel’s value and compared to see which one has the closest value to
the original pixel value with the secret data embedded in. The best candidate is then called the optimal pixel and used
to conceal the secret data (Chan and Cheng, 2004). This however makes the hiding capacity of the carrier image very
low.

2.2 The Pixel Value Differencing (PVD) Method
The pixel-value differencing (PVD) method is proposed by (Wu and Tsai, 2003). In this approach, the payload of
each individual pixel is different, and the resultant stego-image quality is extremely fine with perfect modification
and invisibility. The resultant stego-images quality that the method produces is better in terms of human visual
perception. However steganalysis is easy as the hidden message is not well spread across the entire image.

2.3 Blind Hide algorithm
According to (Bailey, K. and Curran, K., 2006), this algorithm blindly hides the secret data in the image starting at
the top left corner of the image and working its way across the image (then down - in scan lines) pixel by pixel
changing the least significant bits of the pixel colors to match the message. To extract the hidden information, the
least significant bits starting at the top left are read off. This embedding procedure is not very secure as it's really
easy to read off the least significant bits starting from the top left corner of the image sequentially.

2.4 Algorithm Pixel Swap
This method is proposed by Lee et al. (2010). It works as follows
 Randomly select 2 pixels x1 and x2 from the cover image using a pseudo–random sequence.
 If the two pixels lie within a specified distance α (α=2 or 3 generally), they are suitable for embedding,
otherwise generate another set of pixels.
 Take the specific message bit to hide. If the message bit is zero, check if x1 > x2 otherwise swap x1 and x2
and hide the bit in the LSB of the pixel. Do the reverse operation if the message bit is one.

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 For extracting the hidden message, select the pixels using the same pseudo-random sequence. Check if the 2
pixels are within the pre-specified range α. If x1>x2, the message bit is zero (one) otherwise the message bit
is one (zero).
This method does not add visible distortions to the cover image since only one bit is changed per pixel but its
hiding capacity is highly limited.

3. The Proposed Method
The standard minimal linear Congruential Generator (LCG) method (Park and Miller., 1988) is used to generate the
pseudo random numbers used to match the specific bits in the cover image where the secret data bits are hid. This is
one of the most successful random number generators particularly with computer memory.
The formula is explained below.

Xn+1 = (aXn + c) mod m

Where:
X0 is the starting value , the seed ; 0 <=X0 < m
a is the multiplier; a ≥ 0
c is the increment; c ≥ 0
m is the modulus; m > X0, m > a, m > c

The desired sequence of random numbers < Xn > is then obtained by setting
Xn+1 = (aXn + c) mod m, n ≥ 0

Xn is chosen to be in [0, m-1], n ≥0

Given that the previous random number was Xi, the next random number Xi+1 can be generated as follows.
X i+1 = f (Xi, X i-1,…., X i-n+1)(mod m) = (aix i+ a2 x i-1 +…+ an x i-n+1 + c )(mod m)

The communicating partners share a stego key (k) which in this case is the message digest of the user supplied
password.

According to Hull & Dobell (1972), a linear congruential sequence defined by m, a, c and X0 has full period if and
only if the following three conditions hold:

 The only positive integer that exactly divides m and c is 1
 If q is a prime number that divides m, then q divides a -1
 If 4 divides m, then 4 divides a -1

Additionally, the value of m should be rather large since the period cannot have more than m elements. The value of
m should also necessitate a fast computation of (aXn+c) i.e speed the generation of random numbers. Observing all
these requirements, the parameters for the LCG used in this research are as follows:

3.1 Modulus (m)
The 48-bt computer word length was picked as the value of m. Any Pentium IV and above computer should have this
word length or larger. This in essence provides the size of m to be 248 which is equivalent to 281,474,976,710,656.
For the sake of this experiment and bearing in mind that the digital images used are a few kilobytes in size, this
period is sufficient enough to set up the experiment.
To ensure faster generation, m is recommended to be a power of 2 or close to a power of 2 and hence the choice of
the word length. The AND operation also enhanced speed instead of the normal division operation which is
considered slower.

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Vol 2, No.9, 2012

3.2 The seed (X0)
The first value of the seed (X0) is supplied by the message digest of the user supplied password. This is done using a
special form of encryption that uses a one-way algorithm which when provided with a variable length unique input
(message) will always provide a unique fixed length output called hash, or message digest.
3.3 a (Multiplier) and c (Increment)
To ensure full period and in following with requirements identified above, the values of the multiplier and the
increment are picked as follows.
a (Multiplier) = 25214903917
c (Increment) = 11

The values are used to initialize the random number generator used for the prototype.
The numbers generated by this PRNG determines the specific bits in the pixel bytes of the cover image where data
bits of the secret data file are to be embedded. For example considering storing the number 200, whose binary
representation is 11001000 in a grid of 3 pixels of a 24-bit image, utilizing a single LSB of each color channel the
enhanced LSB algorithm will store the significant bits of the message randomly into the cover image bits as shown in
figure 1 and figure 2 below.

0 0 1 0 1 1 0 1 0 0 0 1 1 1 0 0 1 1 0 1 1 1 0 0

1 0 1 0 0 1 1 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0

1 1 0 1 0 0 1 0 1 0 1 0 1 1 0 1 0 1 1 0 0 0 1 1

Figure 1. Original Image Bits

0 0 1 0 1 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1 1 1 0 0

0 0 1 0 0 1 1 0 1 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0

1 1 0 0 0 0 1 1 1 0 1 0 1 1 0 0 0 1 1 0 0 0 1 0

Figure 2. Modified Image Bits

4. Research Method
The study adopted an experimental research method to measure the effect of using selected varied and random pixels
during the embedding process on imperceptibility and hiding capacity. This method represents the standard practice
applied in manipulating independent variables in order to statistically analyze the generated data to test research
hypotheses. A notable advantage of experimental research is the fact that it enables other researchers to easily
replicate the experiment and be able to validate the results. It is therefore considered an accurate method of research
(Shuttleworth, 2008), as the researcher can effectively establish a causal relationship between variables by
manipulating independent variable(s) to assess the effect upon dependent variable(s).
The effect of dispersing the significant bits of the hidden message across a cover image during the embedding
process represented the variable that we weree to understand and study. Thus an experiment was carried out to test
the relationship between the specific embedding process ( i.e. Proposed method) and the outcome ( ie

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imperceptibility level). Essentially the output of traditional least significant bit steganography method was used to
evaluate the performance and effectiveness of the proposed method’s output by comparing the stego images
generated by the proposed method with those generated by the traditional least significant bit steganography method.
This is commonly referred to as comparative experiment (Hinkelmann and Kempthorne, 2008).

5. Algorithm Design Framework

Cover
Image

Encoding Decoding Data File
Data File Stego Key Stego Key
Module Module

Transmission Channel
Stego Stego
Image Image

Figure 3. Framework for the proposed system(Pfitzmann, 1996)
5.1 Embedding and Extraction Algorithms

Input : Cover Image, Secret file (Payload)
Output : Stego image (image containing hidden file)
16. Use LCG to
Select a random pixel
Select a random pixel color channel
Select a random color channel bit
2. Let bitToWrite [x][y][channel][bit] denote the selected bit in a specific color channel for writing
3. Let mi denote the message bit embedded in a color channel bit, bitToWrite[x][y][channel][bit]
4. For all image color channels do the following
5. If LSB (bitToWrite[x][y][channel][bit]) = mi then
6. do nothing
7. If LSB(bitToWrite[x][y][channel][bit]) not equal to mi then
8. bitToWrite[x][y][channel][bit] = mi
9. while secret file length; Repeat step 16 to 23 to embed the entire message
10. Close stream.

Figure 4. Embedding Algorithm

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Vol 2, No.9, 2012

Input : Stego Image, Password message digest
Output : Secret file

1. Use LCG to
Select a random pixel
Select a random pixel color channel
Select a random color channel bit
2. Let bitToRead([x][y][channel][bit])denote the selected bit in a specific color channel for reading
3. Let mi denote the message bit read in a color channel bit bitToRead([x][y][channel][bit])
4. For all image color channels do the following
5. If LSB(bitToRead([x][y][channel][bit]) not equal to mi then
6. do nothing
7. If LSB (bitToRead([x][y][channel][bit]) = mi then
8. bitToRead ([x][y][channel][bit])= mi
9. Pack bit in bitSet
10. While secret file length; Repeat step 11 to 20 to read the entire file
11. Close stream.
12. Obtain the entire message stream and convert it back into ASCII format

Figure 5. Extraction Algorithm
6. Experimental Design and Testing

An analysis to examine the statistical properties of the stego images produced by the proposed method and the
traditional LSB method was carried out. Statistical attacks are more powerful than visual attacks as they are able to
reveal the tiniest modifications in the statistical properties of an image (Artz, 2001).
The following image quality metrics were employed for this purpose:

6.1 Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE)
Both of these metrics are the most common and widely used full reference metrics for objective image quality
evaluation. In particular, PSNR is used in many image processing applications and considered as a reference model
to evaluate the efficiency of other objective image quality evaluation methods (Wang et al., 2002b). PSNR as a
metric computes the peak signal-to-noise ratio, in decibels, between two images. It is used in steganography to
measure the peak signal-to-noise ratio in the original image and the stego image after embedding the hidden data. In
the literature, PSNR has shown the best advantage almost over all other objective image quality metrics under
different image distortion environments and strict testing conditions (Wang et al., 2002a).

On the other hand, MSE measures the statistical difference in the pixel values between the original and the
reconstructed image (Stoica et al., 2003; Wang etal., 2003). The mean square error represents the cumulative squared
error between the original image and the stego-image.

A lower MSE value means a better image quality ie lesser distortion in the cover image while the higher the PSNR
value the better the quality of the image. (Mei Jiansheng et al .2009). PSNR and MSE are defined as shown in
equations (1) and (2) below respectively (Stoica et al., 2003; Wang et al., 2003):

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PSNR = 10.Log10 I 2/MSE db (1)

M N
∑ ∑ (Xij - Xij )2 (2)
MSE = ( 1/MN)
i=1 j=1

Where:
Xij is the ith row and the jth column pixel in the original (cover) image,
Xij is the ith row and the jth column pixel in the reconstructed (stego) image,
M and N are the height and the width of the image,
I is the dynamic range of pixel values, or the maximum value that a pixel can take, for 8-bit images: I=255.

6.2 Reed-Solomon (RS) analysis
This is a method proposed by Fridrich for detecting the use of LSB steganography (Fridrich et al,.2001). RS
measures the smoothness of the changes among pixels of an image (the lower the value, the smoother the changes
among them, or the lesser the noise of the image). RS is one of the most reliable quantitative steganalysis methods
(Fridrich et al,.2001).

In order to evaluate the performance of the proposed method an experimental design was set up. Stego images from
both the traditional LSB method and the proposed method were compared using the testing metrics discussed above.
All the experiments were implemented and run on a PC Pentium IV Duo core, 2.1 GHz with 2GB of RAM under the
Windows 7 Home Edition operating system. The following constants were ensured.
 Same images were used on both the methods
 Same information was embedded in each image ie equal payload
 Same evaluation metrics were used for each image
 Five digital images were used as test data files (cover images). Table 2 shows the list of these digital
images.

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Table 2. Test data Images

FILE NAME DIMENSIONS FILE SIZE

Banana.jpg 685 x 514 Pixels 157 Kilo Bytes

Dancers.jpg 685 x 457 Pixels 224 Kilo Bytes

Graduation.jpg 685 x 457 Pixels 161 Kilo Bytes

Office.jpg 685 x 457 Pixels 163 Kilo Bytes

zhbackground.bmp 685 x 610 Pixels 1.19 MB

The specific data hiding steganogrtaphic method used was taken to be the independent variable (in this case the
traditional LSB method and the proposed enhanced LSB method). In order to evaluate the efficiency of the proposed
steganography method, the evaluation dependent variables all of which measure the image distortion levels were
considered. Accordingly, for each steganography method (the traditional LSB method and the proposed enhanced
LSB method) and for each cover image the value of each dependent variable was measured. The values of the
dependent variables for both embedding methods were then compared.

7. Experimental Results

7.1 Peak Signal to Noise Ratio (PSNR)
Figure 5 below shows the comparison of the PSNR of the five stego images for both the traditional LSB method and
the enhanced LSB method.

db

Images

Figure 5: The PSNR (db) of stego images hiding in (Traditional LSB) vs hiding in (Enhanced LSB)

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Every image tested registered a higher PSNR for enhanced LSB method as compared to the Traditional LSB method
showing that the enhanced LSB embedding method distorts the image less improving on imperceptibility of the
hidden data since a higher Peak Signal to Noise Ratio (PSNR) indicates less distortion (Mei Jiansheng et al,.2009).

7.2 Mean Square Error (MSE)
Figure 6 below shows a summary of the comparison of the MSE of five stego images for both the traditional LSB
method and the enhanced LSB method.

For each stego image, a lower MSE was recorded with the enhanced LSB method as compared to the traditional LSB
method. A lower MSE value means a better image quality ie lesser distortion in the cover image (Mei Jiansheng et
al,.2009). This means that stego images generated by the enhanced LSB method have lesser distortions compared to
those generated by the traditional LSB method and hence improved imperceptibility.

db

IMAGES
Figure 6: The MSE of stego images Hiding in (Traditional LSB) vs Hiding
in (Enhanced LSB)

7.3 Reed-Solomon (RS) analysis
Stego images generated by the enhanced LSB method all recorded lower values of RS compared to those generated
by the traditional LSB method as shown in figure 7. Therefore these images have lesser noise and the information
hidden in them more imperceptible (Fridrich et al,.2001) .

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RS

IMAGES
Figure 7: The RS of stego images hiding in (Traditional LSB) vs hiding in (Enhanced LSB)
9. CONCLUSION AND FUTURE WORK
In comparison to the traditional least significant bit algorithm, the data hiding steganographic method presented in
this paper was found to demonstrate increased imperceptibility to statistical steganalysis attacks on the cover image.
The hiding capacity can also be increased by varying the number of bits used per color channel. However this
method is best suited for the purposes of communication and communication applications as more permanent aspects
of steganography like watermarking are not included.
As with other steganographic applications, the cover images used should be high quality original photographs. The
recommended mode of transmission of the stego images is through web postings or email attachments.
Digital steganography is a rapidly growing and increasingly interesting field of research for information hiding and
data security. It is currently playing a vitally important role in defense and civil applications. In future, we are bound
to see more of data security applications based on this technology.
In relation to this research, future work should centre on development of stronger embedding algorithms whose
output can survive image manipulations and those that can make use of more permanent embedding procedures. This
will facilitate the use of steganography in more sensitive application areas like in computer digital forensics and in
enhancement of security in electronic commerce and trading applications. Research along these lines will also help in
ensuring a permanent solution to the issues of plagiarism of copy write digital content materials.

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References
Artz D. (2001) “Digital steganography: hiding data within data”, Internet Computing, IEEE, vol. 5, Issue: 3, pp.
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Bailey, K. and Curran, K. (2006) An Evaluation of Image Based Steganography Methods Using Visual Inspection
and Automated Detection Techniques. Multimedia Tools and Applications, 31, 55-88.

Chan, and Cheng, L.M. (2004). Hiding data in images by simple LSB substitution. Computer Journal of Pattern
Recognition Letters, vol. 37, no. 3, pp. 469-474
Fridrich, J., Goljan, M. & Hogea, D. (2002) Attacking the OutGuess. The ACM Workshop on Multimedia and
Security.

Lee, Y.K., Bell, G., Huang, S.Y., Wang, R.Z. and Shyu, S.J. (2010). An Advanced Least-Significant-Bit Embedding
Scheme for Steganographic Encoding. Advances in Image and Video Technology. Berlin / Heidelberg: Springer,
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Hinkelmann, K. & Kempthorne, O. (2008) Design and Analysis of Experiments: Introduction to Experimental
Design, John Wiley & Sons, Inc., Hoboken,New Jersey.

Mei Jiansheng, Li Sukang and Tan Xiaomei,(2009) “A Digital Watermarking Algorithm Based on DCT and DWT”,
in Proceedings of the 2009 International Symposium on Web Information Systems and Applications (WISA’09)
Nanchang, P. R. China, pp. 104-107.

Mohammad Fahmi, Alalem Abdallah , Muhanah Manasrah,(2008) “A Steganographic Data Security Algorithm
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Park,S.K, and Miller, K.W. (1988). "Random Number Generators: Good Ones Are Hard To Find". Communications
of the ACM 31 (10): 1192–1201.

Pfitzmann, B. (1996). ‘Information Hiding Terminology’, In:Information Hiding: First International Workshop (R
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Wu, D.C. and Tsai, W.H. (2003). A steganographic method for images by pixel value differencing. Pattern
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Shuttleworth, M. (2008) Experiment Resources. Accessed: September 25, 2011. [Online] Available:
http://www.experiment-resources.com. ( July 10, 2012)

Stoica, A., Vertan, C. and Fernandez-Maloigne, C. (2003) Objective and subjective color image quality evaluation for
JPEG 2000 compressed images. International Symposium on Signals, Circuits and Systems, SCS 2003, 1,137-140.

Wang, Z., Bovik, A. C. and Lu, L. (2002) Why is image quality assessment so difficult? IEEE International
Conference on Acoustics, Speech, and Signal Processing, (ICASSP '02), 4, 3313-3316.

Wang, Z., Sheikh, H. R. and Bovik, A. C. (2002b) No-reference perceptual quality assessment of JPEG compressed
images. Proceedings of the International Conference on Image Processing, 1, 477-480.

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